Microsensor system and method for measuring data

ABSTRACT

One embodiment is a microprobe. An example of the microprobe comprises a housing having an aperture. This example of the microprobe also comprises an ISFET attached to the housing. The ISFET may have a gate located proximate the aperture. This example of the microprobe further comprises a reference electrode attached to the housing proximate the aperture. Another embodiment is a microsensor system. Another embodiment is a method for measuring a characteristic of tissue. Yet another embodiment is a method for monitoring tissue pH.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 10/762,133, filed 20 Jan. 2004, titled “Microsensor System andMethod for Measuring Data”, pending, which claims the benefit of U.S.Provisional Patent Application No. 60/465,298, filed 25 Apr. 2003,titled “Method and Apparatus for Medical Data Surveillance”, both ofwhich are incorporated herein by this reference. U.S. patent applicationSer. No. 10/762,133 is a continuation-in-part of U.S. patent applicationSer. No. 10/423,568, filed 25 Apr. 2003, titled “Method and System forDetecting Changes in Data”, pending, and is a continuation-in-part ofU.S. patent application Ser. No. 10/655,631, filed 29 Aug. 2003, titled“System and Method for Improved Patient Status Monitoring”, nowabandoned, both of which are incorporated herein by this reference.

BACKGROUND

1. Technical Field

The present invention relates to monitoring data, and in someembodiments may concern assessing the medical condition of a patient.More particularly, some examples of the invention concern using asubcutaneous sensor to measure a characteristic of tissue in a patient,and in some examples may be used to measure tissue pH to detect shock,or to determine if tissue is viable.

2. Description of Related Art

Improved casualty/patient care is vital to military operationalpractices, and is also of great importance in many civilianenvironments. For example, frequently it is important for a medical careprovider to be able to quickly and accurately assess the medicalcondition of a patient in a combat environment, during disaster relief,at an accident scene, during patient transport, in an emergency room, ata hospital, or at any other location during a medical emergency or thegeneral treatment of a patient.

Shock is one of the body's biochemical reactions to an injury. Thepresence of hypoglycemic or hemorrhagic shock, which frequently occursin combat casualties and accident victims, must be rapidly andaccurately assessed, and must be monitored, to ensure the bestprognosis, final patient disposition, and maximum survival rates.

Detecting depression of a patient's vital signs (blood pressure,respiration and heart rate), is a known method for detecting shock. Thistechnique may be used in the field and other environments. However, thismethod is imprecise and is not favored if other methods are available.

Two methods are known that generally have been used for monitoring theshock state of a patient in a hospital environment. In the first method,global blood flow assessments are made by measuring arterial P_(a)(O₂)oxygen delivery to the body. In the second method, arterial bloodlactate concentrations and oxygen consumption are measured. Both ofthese techniques are invasive and hence may negatively impact thepatient's health, because they generally require drawing blood with acatheter (perhaps from the stomach), and also require sophisticatedequipment, and consequently cannot be used in the field.

Tissue tonometry is a tissue oriented approach to measuring anddiagnosing the shock state of a patient. Tissue P(O₂) levels, tissueP(CO₂) levels, and tissue pH, have been shown to be reliable estimatesof a compensated shock state. However, the use of these approaches hasbeen hampered by the lack of a reliable and noninvasive method ofobtaining accurate measurements.

In combat, disaster, and accident scenarios, the extent of injury to apatient may be so severe as to require amputation of gangrenous tissue.Gangrenous tissue is tissue that has died due to lack of blood supply.Dead tissue may also be present on frostbite victims. Generally, thegoal of an amputation is to remove all dead tissue. The extent of tissueremoval during an amputation may be excessive, because medical personnellack an accurate means of determining the demarcation between viable andnon-viable tissue.

In summary, known methods of measuring the shock state of a patient areinadequate because they are invasive, non-portable, and/or inaccurate.Further, known methods for identifying and monitoring the border betweenviable and non-viable tissue are not sufficiently accurate to precludeneedless amputation of viable tissue. Additionally, there is no knownmeans of monitoring the continued viability of tissue after anamputation.

SUMMARY

One embodiment is a microprobe. An example of the microprobe comprises ahousing having an aperture. This example of the microprobe alsocomprises an ISFET attached to the housing. The ISFET may have a gatelocated proximate the aperture. This example of the microprobe furthercomprises a reference electrode attached to the housing proximate theaperture.

Other embodiments are described in the sections below, and include, forexample, a microsensor system, a microsensor array system, a method formonitoring data (which in some examples may be a characteristic oftissue), and a signal bearing medium tangibly embodying a program ofmachine-readable instructions executable by a digital processingapparatus to perform a method for monitoring data (which is someexamples may be a characteristic of tissue).

Some embodiments provide for portably, noninvasively, and accuratelymeasuring and monitoring the shock state of a patient, and further, someembodiments may accurately identify viable and non-viable tissue.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the hardware components andinterconnections of a patient status monitor in accordance with anexemplary embodiment.

FIG. 2 is a block diagram of a patient status data processor inaccordance with an exemplary embodiment.

FIG. 3 is a block diagram of a simple threshold test processor inaccordance with an exemplary embodiment.

FIG. 4 is a block diagram of a Dynamic Change point Detection analysisprocessor in accordance with an exemplary embodiment.

FIG. 5 shows an exemplary signal-bearing medium in accordance with anexemplary embodiment.

FIGS. 6A-C are a flowchart of an operational sequence for monitoringpatient status in accordance with an exemplary embodiment.

FIG. 7 is a time-line event graph in accordance with an exemplaryembodiment.

FIG. 8 is a Dynamic Change point Detection graph in accordance with anexemplary embodiment.

FIG. 9 is a data analysis report in accordance with an exemplaryembodiment.

FIG. 10 is another example data analysis report in accordance with anexemplary embodiment.

FIG. 11 is a Dynamic Change point Detection graph displaying unequaltime interval data in accordance with an exemplary embodiment.

FIG. 12 is a Dynamic Change point Detection graph displaying smoothedoutput and outliers in accordance with an exemplary embodiment.

FIG. 13 is a block diagram of the hardware components andinterconnections of a microsensor system in accordance with an exemplaryembodiment.

FIG. 14 is a diagram of a microprobe in accordance with an exemplaryembodiment.

FIG. 15 is a schematic diagram showing electrical connections tocomponents of a microprobe in accordance with an exemplary embodiment.

FIG. 16 is a depiction of a microprobe delivery system in accordancewith an exemplary embodiment.

FIG. 17 is a depiction of a microprobe delivery system in accordancewith an exemplary embodiment.

FIG. 18 is a diagram of a control module in accordance with an exemplaryembodiment.

FIG. 19 is a cross sectional view of an ISFET sensor in accordance withan exemplary embodiment.

FIGS. 20A-C are a flowchart of an operational sequence for fabricating amicrosensor system in accordance with an exemplary embodiment.

FIGS. 21A-B are a flowchart of an operational sequence for measuring acharacteristic of tissue in accordance with an exemplary embodiment.

DETAILED DESCRIPTION

The nature, objectives, and advantages of the invention will become moreapparent to those skilled in the art after considering the followingdetailed description in connection with the accompanying drawings.

I. Introduction

The present invention generally relates to monitoring data. Someembodiments provide a microvascular technique for measuring acharacteristic of tissue in a patient. Some specific examples concerninjecting a microprobe in dermal tissue, to quickly and relativelynon-invasively measure dermal tissue pH. Although an embodiment formeasuring tissue pH is discussed in this introduction, the invention isnot limited to this specific illustrative example.

Real-time measurement of dermal tissue pH (the pH of the vascular layerof the skin), can be used to monitor the general onset of shock. Someexamples of the invention provide a real-time microsensor system formeasuring tissue pH in the vascular layer of skin, and consequently maybe used to detect and provide an early warning for the onset ofhypoglycemic or hemorrhagic shock. Thus, the microsensor system providesa relatively non-invasive, portable instrument for monitoring thegeneral onset of shock. The microprobe may be implanted in the dermis inalmost any location on a human body, including the fingers and toes,which are desirable locations for measuring shock.

The microsensor system may also be used to identify viable tissue, forexample before, during, and/or after an amputation or other surgery. Oneimportant application is identifying and/or monitoring viable andnon-viable tissue in frostbite patients. Another important applicationis identifying and/or monitoring gangrenous and non-gangrenous tissue.Another application is identifying and/or monitoring non-viable tissueafter a patient has been exposed to severe radiation. In circumstancewhere non-viable tissue must be amputated, a plurality of microprobescould be placed in different locations in patient tissue, to gatherinformation that may be presented as a three dimensional representationof the patient tissue, so that a surgeon can determine precisely whichtissue to remove (and also which tissue that possibly may be revived).

The microsensor system could also be used to determine whether there isadequate capillary fill after removing a tourniquet. In another example,the microsensor system could be used to detect and/or monitor indicatorsof heart blockage (may be indicated by no change after implementation ofan intervention) or congestive heart failure. Statistical algorithms maybe performed using data gathered over a period of time, to identifytrends and other useful information in the data.

As an example, the microsensor system with or without the patient statusmonitor could be routinely used by medics and corpsmen on thebattlefield, and also could be used by surgeons and other personnel atforward medical units. The microsensor system with or without thepatient status monitor could also be used in disaster scenarios, and byemergency medical teams. The microsensor system with or without thepatient status monitor could be used to assist in establishing patienttriage, and generally could result in improved urgent care and increasedpatient survival rates. The microsensor system and the patient statusmonitor generally provide enhanced functionality, sensitivity, andaccuracy.

II. Definitions

The following acronyms and definitions are used herein:

Acronyms:

Ag—Silver

AgCl—Silver Chloride

Al—Aluminum

CD-ROM—Compact Disc-Read Only Memory

CD-R—Compact Disc-Read

CD-RW—Compact Disc-Read Write

CUSTUM—Cumulative Sum

DVD-R—Digital Versatile/Video Disc Recorable (write once storage)

DVD+R—Digital Versatile/Video Disc Recorable (write once storage)

DVD-RW—Digital Versatile/Video Disc Read/Write (write multiple timesstorage)

DVD+RW—Digital Versatile/Video Disc Read/Write (write multiple timesstorage)

EPROM—Erasable Programmable Read-Only Memory

EEPROM—Electrically Erasable Programmable Read-Only Memory

FIR CUSUM—Fast Initial Response Cumulative Sum

FIR MAX CUSUM—Fast Initial Response Maximum Cumulative Sum

ICD-9—International Classification of Diseases-Revision 9

ISFET—Ion Sensitive Field Effect Transistor

MAX CUSUM—Maximum Cumulative Sum

MEMS—MicroElectroMechanical System

MOSFET—Metal-Oxide-Semiconductor Field Effect Transistor

RAID—Redundant Array of Inexpensive Discs

RAMAC—Random Access Method of Accounting and Control

RAM—Random Access Memory

ROM—Read Only Memory

Si—Silicon

Si₃N₄—Silicon Nitride

SiO₂—Silicon Dioxide

SOI—Silicon-On-Insulator

SOS—Silicon-On-Sapphire

WORM—Write Once Read Many (write once storage)

Definitions:

β (Beta):

β is a constant, μ₂/(2σ), the minimum number of standard deviations fromthe mean the Markov sums will pick up a change in state. As an example,β may be set equal to 0.5

Baseline:

The baseline is an estimate of the background noise only condition.

Burst point:

δ and γ are the Shewhart test thresholds where γ>δ, for example, δequals 2 and γ equals 3. A burst point is a data point at least γstandard deviations away from the baseline mean. (The test is adjustedfor small sample sizes.)

Data point i (or x_(i)):

The ith data input set associated with a data type consisting of a data,time, and value.

Data type:

Type of data analyzed such as pulse rate, blood pressure, respiration,body temperature, or white blood cell count for which there isinformation for a patient.

δ (Delta):

δ and γ are the Shewhart test thresholds where γ>δ, for example, δequals 2 and γ equals 3.

ω is the approximate ratio of the standard normal threshold and thet-test threshold associated with the degrees of freedom in the mean andstandard deviation calculations. ω is calculated as follows:

$\overset{\_}{\omega} = \sqrt{\frac{n - r}{n - r + 1}}$F:F is the noise only distribution and f is the derivative of F in thederivation of the cumulative sum statistics.FIRSH:FIRSH is the Fast Initial Response cumulative upper sum.FIRSH max:FIRSH max is the Fast Initial Response Maximum cumulative upper sum.FIRSL:FIRSL is the Fast Initial Response cumulative lower sum.FIRSL max:FIRSL max is the Fast Initial Response Maximum cumulative lower sum.G:G is the noise plus signal distribution and g is the derivative of G inthe derivation of the cumulative sum statistics.γ (Gamma):δ and γ are the Shewhart test thresholds where γ>δ, for example, δequals 2 and γ equals 3.H:H is the CUSUM statistic test threshold. H is a constant chosen suchthat the reciprocal of the average run length equals the desiredprobability of false alarm.Input data:The data associated with a data type for a particular patient. Inputdata may consist of sets of date, time, and value information.K:Index of the data point where the signal starts.L:Index of the data point where the signal ends.m:m is a positive integer that represents the number of data points thathave been input and received into the process.Mean:The mean is the average of the conforming data points and is calculatedusing the formula:

${mean} = \frac{\sum\limits_{i = 1}^{m}\;{R\left( x_{i} \right)}}{m - r}$μ (Mu):Mu is the population mean.n:The number of data points used to create the currently in-use baselinemean and standard deviation.N(μ₁, σ²):N(μ₁,σ²) normal distribution with mean μ₁ and standard deviation σ.N(μ₂,σ²):N(μ₂,σ²) normal distribution with mean μ₂ and standard deviation σ.Outlier:δ and γ are the Shewhart test thresholds where γ>δ, for example, δequals 2 and γ equals 3. A outlier is a data point at least δ standarddeviations away from the baseline mean. (The test is adjusted for smallsample sizes.)r:The number of data points removed from consideration either a priori bythe end-user or because they are found to be non-conforming data points.R(X_(i)):The function of the incoming observations R(X_(i)), is used to removeuser specified and non-conforming data points from the baselinecalculation

${\sqrt{\frac{i - r}{i - r + 1}}{\frac{X_{i} - {mean}}{sd}}}\underset{\_}{>}2$or if the user has specified that x_(i) is to be removed; and is equalto x_(i), otherwise.S:S is the cumulative sum associated with unspecified F and Gdistributions in the cumulative sum statistic derivation.sd:sd is the standard deviation and is calculated using the formula:

${sd} = \sqrt{\frac{\sum\limits_{i = 1}^{m}\;\left( {{R\left( x_{i} \right)} - {mean}} \right)^{2}}{m - r - 1}}$SH:SH is the CUSUM upper sum and for the ith data-point is calculated asfollows:SH _(i)=max(SH _(i−1) +{circumflex over (t)} _(i)−β,0)The Fast Initial Response statistic is created by initializing thecumulative sum to H/2.SH max:SH max is the MAX CUSUM upper sum statistic and for the ith data-pointis calculated as follows:

${sd} = \sqrt{\frac{\sum\limits_{i = 1}^{m}\left( {{R\left( x_{i} \right)} - {mean}} \right)^{2}}{m - r - 1}}$where SH₀=0.The Fast Initial Response statistic is created by initializing thecumulative sum to H/2.Shift_(i, lower):The negative signal amplitude or downward shift for the ith data pointis estimated as follows:

${shift}_{i,{lower}} = {\left( {\frac{{SL}_{i}}{{length}_{i,{lower}}} + \beta} \right)(f)({sd})}$Shift_(i, upper):The positive signal amplitude or upward shift for the ith data point isestimated as follows:

${shift}_{i,{upper}} = {\left( {\frac{{SH}_{i}}{{length}_{i,{upper}}} + \beta} \right)(f)({sd})}$δ (Sigma)Sigma is the population standard deviation.SL:SL is the CUSUM lower sum and for the ith data-point is calculated asfollows:SL _(i)=max(SL _(i−1) {circumflex over (t)} _(i)−β,0)

The Fast Initial Response statistic is created by initializing thecumulative sum to H/2.

SL max:

SL max is the MAX CUSUM lower sum statistic and for the ith data-pointis calculated as follows:

${{SL}\;\max_{j}} = {\max\limits_{{i = 1},{\ldots\mspace{20mu} j}}\left( {SL}_{i} \right)}$where SL₀=0.The Fast Initial Response statistic is created by initializing thecumulative sum to H/2.S max:S max is the maximum cumulative sum associated with unspecified F and Gdistributions.t:t is the standard score. The approximate standard score is calculated asfollows:

${\hat{t}}_{i} = {\left( \overset{\_}{\omega} \right)\left( \frac{X_{i} - {mean}}{sd} \right)}$

III. Hardware Components and Interconnections A. Patient Status Monitor

Before discussing the hardware and the operation of a microsensorsystem, below, an embodiment of an apparatus for monitoring data willfirst be discussed. As a specific example of this apparatus, anapparatus for monitoring patient status will be discussed. With somemodifications to the example below, it is understood that “patient”status can in some cases additionally be more generally understood asthe status of the system that is being monitored, which may include, forexample, a manufacturing environment and the data produced from such forquality control, the stock market or other economic systems, or apopulation group (which may include plants, bacteria, etc.) beingmonitored for disease or exposure to agents injurious to their or itshealth. The apparatus for monitoring patient status may be used inconjunction with, or may be incorporated into, the microsensor system.As an example, the apparatus for monitoring patient status may beembodied by the hardware components and interconnections of the patientstatus monitor 10 shown in FIG. 1. The patient status monitor 10includes a patient status data processor 12.

One or more data collection devices are coupled to the patient statusdata processor 12, for sensing or otherwise receiving data. As anexample, the data collection devices may include one or more medicalsign sensors 14, one or more medical lab data test sensors 16, and/orone or more intervention data collectors 18. One or more of the medicalsign sensors 14 may be a microprobe 1302 (shown in FIG. 13), or amicroprobe system 1300 (shown in FIG. 13), (which may gather tissue pHinformation and possibly other information), and which will be discussedbelow.

The patient status data processor 12 may be coupled to one or moreoutput devices, which may include, for example, one or more displays 20,one or more printers 22, and/or one or more data archives 24. Display 20may include a visible and/or audible alarm and/or other means to conveyan alarm condition. For example, the display may include a speaker orbuzzer or other audio transducer for producing an audio alarm. Asanother example, a visible alarm may be presented on the display, or aLED could be included on the display housing for indicating an alarm.The medical sign sensor 14 and medical lab data test sensors 16 sense orotherwise receive information such as, for example the following datatypes, pulse rate, blood pressure, respiration, body temperature, whiteblood cell count, iron, cholesterol, triglyceride, blood sugar, tissuepH, and blood gas information for a patient, and output data valuesrepresentative of the medical signs and lab tests, as data to thepatient status data processor 12. The intervention data collector 18gathers information concerning actions taken to correct problems, upperand lower patient reference values, upper and lower patient thresholdvalues, and other significant actions as well as how long a data typevalue can remain unchanged before an alarm should sound that anintervention is not working. For example, the intervention datacollector 18 may receive data concerning when medication is administeredor when the patient was covered with a blanket. The intervention datamay be date/time stamped.

The patient status data processor 12 may be any type of data processingapparatus, and as an example, may be a microprocessor or computer. Thecomputer may be, for example, a personal computer, personal digitalassistant, mainframe computer, computer workstation, supercomputer, orother suitable machine. As shown in FIG. 1, the patient status dataprocessor 12 may include a processor 26, such as a microprocessor orother processing machine, which may be coupled to a memory 28 and anonvolatile storage 30. The memory 28 may comprise random access memory,and may be used to store programming instructions executed by theprocessor 26. The nonvolatile storage 30 may comprise, for example, oneor more magnetic data storage disks such as a “hard drive,” an opticaldrive, a tape drive, or any other suitable storage device. The patientstatus data processor 12 may also include an input/output 32, such as aline, bus, cable, or electromagnetic link, for the data processor 12 toreceive instructions from a user, and may also permit exchanging datawith locations external to the patient status data processor 12.Alternatively, the memory 28 and/or the nonvolatile storage 30 may beeliminated, and further, memory and/or nonvolatile storage may beprovided on-board the processor 26, or externally to the patient statusdata processor 12.

FIG. 2 is a block diagram of an example of a patient status dataprocessor 12. The data processor 12 may include an initialization module202, for initializing values used in the subsequent processing of data.The data processor 12 also includes a module 204 for receiving data. Asan example, the data may include medical signs and/or lab test resultsand/or intervention information. The data processor 12 also may includea time-line event analysis module 206, a Dynamic Change point Detectionprocessor 208, and/or a Simple Threshold Test module 210. The dataprocessor 12 may also include a forecasting module 212, and a module 214for generating report(s) and/or graph(s) and/or for transmitting data todesired external devices or users.

FIG. 3 is a block diagram of an example of a simple threshold testprocessor 210. As shown in FIG. 3, the simple threshold test processor210 may include an initialization module 302, for initializing valuesused for performing threshold tests. The threshold test processor 210may also include a module 304 for receiving data. As an example, thedata may include medical signs and/or lab test results and/orintervention information. The threshold test processor 210 may alsoinclude a user input selection module 306, and a user input displaymodule 308. The threshold test processor 210 may also include a module312 for storing patient reference values. The threshold test processor210 also includes a threshold test module 310 for performing thethreshold tests. The threshold test processor 210 may also include amodule 314 for generating tabular or graphic report(s) and graph(s)and/or transmitting data to desired external devices or users.

FIG. 4 is a block diagram of an example of a Dynamic Change PointDetection processor 208. The Dynamic Change point Detection processor208 may include a user input selection module 402, into which a user mayinput information such as a data type (eg. tissue pH, or systolic bloodpressure) to be analyzed, a start date, and an end date. The DynamicChange point Detection processor 208 may also include an output display404 of the user selections, and a module 406 for storing data referencevalues (for example, patient reference values). The Dynamic Change pointDetection processor 208 also includes the Dynamic Change point Detectionanalysis algorithm module 408, and may also include an output buffer 410and a report and graph generator 412 for generating an output display ofthe results in tabular and graphic form and/or for transmitting data toan external device or user. The Dynamic Change point Detection analysisalgorithm processor 208 may also include a link (not shown) to a spreadsheet for viewing a subset of the data.

B. Microsensor System

FIG. 13 is a block diagram of an example of a microsensor system 1300,which in one example may be used to measure tissue pH, and which in someexamples may be called a tissue pH microsensor system. The exemplarymicrosensor system 1300 includes a microprobe 1302, a microprobedelivery system 1303, a control module 1304, and interface 1306, whichoperably couple the microprobe 1302 to the control module 1304.Interface 1306 may include electrical wires, optical fiber or wirelessdata and/or power transmission means.

B1. Microprobe

FIG. 14 is a diagram of the microprobe 1302 (which may be called atissue microprobe). The microprobe 1302 must be small enough to notcause an adverse reaction in the human body, or a reaction that wouldinterfere with obtaining accurate data. The microprobe 1302 includes ahousing 1402 having an aperture 1404. The housing 1402 may have a tab1403 for limiting the extent of incursion of the microprobe 1302 intothe dermis. The microprobe 1302 also includes an ion sensitive fieldeffect transistor (ISFET) microsensor 1406, which is attached to thehousing 1402 (which may also be called an encapsulant). The ISFET 1406has a gate 1408 located proximate the aperture 1404, as well as a sourceand a drain (shown and discussed with reference to FIG. 19 below). Themicroprobe 1302 also includes a reference electrode 1410 (which may becalled a reference microelectrode), which is attached to the housing1402 proximate the aperture 1404. As an example, the reference electrode1410 may be made of silver (Ag), silver chloride (AgCl), Pt, or withfunctionally equivalent material. At least a portion of the gate 1408and at least a portion of the reference electrode 1410 may be locatedwithin the aperture 1404. However, depending upon the exact structure ofthe microprobe 1302, especially in its microfabricated form, the housing1402 and aperture 1404 may be only functionally present. Said housing1402 may only comprise the substrate upon which all components arefabricated in sequence, such as a sapphire substrate, or an encapsulatelayer, which by example might be a thin SiN4 layer, deposited orotherwise formed in regions that without the encapsulate, would not bebiocompatible. As an example, the housing and the ISFET may beintegrally formed in biocompatible material. As another example, thehousing and the reference electrode may be integrally formed inbiocompatible material. In some examples, the housing may be made ofsapphire. Herein components (for example, the housing, ISFET, referenceelectrode and/or substrate) that are “attached” may be separate piecesthat are secured together, or integrally formed components.

ISFETs have a structure that is similar to metal-oxide-semiconductorfield effect transistors (MOSFETs), which are commonly used in low powermicroelectronics. Generally, in field effect transistors, a current flowoccurs between source and drain regions, in response to modulation ofchannel conductivity by the gate region. In a conventional MOSFET, avoltage is applied to a metal gate to perform the modulation. In thecase of an ISFET, modulation occurs by changing the surface charge on abare gate insulator. The surface charge may change in response to theprotonization-deprotonization of the gate insulator in the presence of asolution. The protonization-deprotonization is directly related to thepH of the solution.

The reference electrode 1410 may be used to complete the electricalcircuit. Direct measurement of the pH of the solution may be obtained bymeasuring a shift in the threshold voltage (the onset of source-draincurrent), or by measuring the source-drain current, of the ISFET. Thecurrent flow may be coupled to a display and scaled to provide a pHreadout. Thus, in some applications the ISFET may be used to measure thepH of tissue. In some examples, the microprobe could also have a bloodgas sensor, which could be a second ISFET, or a single ISFET could beused for sensing blood gas rather than pH. Due to the miniaturization ofthe microprobe system, a plurality of sensing elements or arrays ofelements could be included in the microprobe.

The microprobe 1302 may also include associated electronics circuitry1412 attached to the housing 1402. The associated electronic circuitry1412 need not be placed in or proximate the aperture 1404, and may beprotected inside the housing 1402. In FIG. 14 a breakaway of the housing1402 is indicated to render the associated electronics circuitry 1412visible. As an example, the circuitry 1412 may be a temperature sensingdiode or another type of sensor. In another example, the circuitry 1412could be logic circuitry, which for example could be a logic array or amicroprocessor, which could permit the microprobe 1302 to performstatistical algorithms and other logical and mathematical operations(thereby operating as a smart sensor). The ISFET 1406 and the referenceelectrode 1410, and also if desired the associated circuitry 1412, maybe monolithically integrated. The ISFET 1406 and the reference electrode1410, and also if desired the associated circuitry 1412, may be formedon a substrate 1414 that is attached to the housing 1402. The housing1402 may substantially encapsulate and may hermetically seal thesubstrate 1414 except for a portion of the substrate 1414 within theaperture 1404, wherein at least a portion of the gate 1408 and at leasta portion of the reference electrode 1410, are attached to the substrate1414 within the aperture 1404. Thus, the housing 1402 may hermeticallyseal the ISFET 1406 and the reference electrode 1410, except forportions of the ISFET 1406, the reference electrode 1410 that are withinthe aperture 1404, to permit contact between the components in theaperture 1404 and the tissue being measured. Accordingly, the microprobe1302 defines an exterior space that is exterior to the microprobe 1302,wherein at least a portion of the gate 1408 and at least a portion ofthe reference electrode 1410, are in fluid communication with theexterior space. The microprobe 1302 may be shaped, for example, as asmall needle, as a cylinder, or in any other appropriate shape. Thehousing 1402 may be made of any suitable biocompatible material, and asan example, may comprise silicon nitride.

The microprobe 1302 also has a power/electronic interface 1416, forsupplying power to the microprobe 1302, and for outputting data signalsfrom the microprobe 1302 to the control module 1304. Thepower/electronic interface may be coupled to the ISFET 1406, thereference electrode 1410, and the associated circuitry 1412. In oneexample, the power/electronic interface 1416 is three wires (power,ground, and signal). In other embodiments, one, two, four or more wirescould be used. The wires of the power/electronic 1416 interface may belong enough to connect the microprobe to the control module 1304, or mayextend only a short distance from the microprobe 1302, in which caseadditional wires may be used to connect the microprobe to the controlmodule 1304. Alternatively, the wires of the power/electronic interface1416 may only reach to within the microprobe 1302, and could be attachedto a socket on the microprobe 1302, which could be coupled to additionalwiring to connect the microprobe 1302 to the control module 1304. Othermethods could be used to couple the microprobe 1302 to the controlmodule 1304. For example, the power/electronic interface 1416 couldinclude a fiber-optic interface (and possibly fiber optic cable), and afiber-optic link could be connected between the power/electronicinterface 1416 and the control module 1304, for transmitting data to thecontrol module 1304. In other embodiments, the power/electronicinterface 1416 could include an electromagnetic transmitter (andpossibly a receiver), for permitting untethered communications betweenthe microprobe and the control module, and for leaving the patientuntethered. For example, the electromagnetic communications couldutilize free-space optical, radio-frequency, microwave, or generally anyother suitable frequencies of electromagnetic waves. As an example, datasignals could be wirelessly transmitted from the microprobe 1302 to thecontrol module 1304. In examples of the microsensor system 1300 in whicha plurality of microprobes 1302 are simultaneously utilized, untetheredcommunications may be particularly useful for permitting the controlmodule 1304 to simultaneously monitor the plurality of microprobes 1302,without having a large number of wires or fiber optic cables.

The microprobe 1302 may receive power from the control module 1304, forexample, via one or more wires connected to the microprobe 1302 and thecontrol module 1304. Alternatively, a separate power supply could beused to supply power to the microprobe 1302. Alternatively, themicroprobe 1302 could include a power source 1417, which is coupled tothe ISFET 1406, and which may also be coupled to the reference electrode1410. For example, the power source 1417 could be a battery coupled tothe ISFET 1406, for supplying power to the ISFET. In other examples, thepower source 1417 could include a photo-voltaic electrical generator, aradioisotope-based power generator, a chemical power generator, or akinematic power generator, coupled to the ISFET 1406. In anotherexample, the power source 1417 could include an antenna and a capacitor,wherein the capacitor is coupled to the ISFET 1406, and the antenna iscoupled to the capacitor, and wherein the capacitor is configured tostore electromagnetic energy received by the antenna (for example, apulse or series of waves), to provide power for the microprobe 1302.

The microprobe 1302 could also include a calibrant 1418 (material usedfor calibrating), which could be a liquid, gel, powder, solid, or gas,which is placed in contact with the gate 1408 of the ISFET 1406 and withthe reference electrode 1410, for calibrating and/or testing themicroprobe 1302. Alternatively, the calibrant could be included as partof the microprobe delivery system 1303 (discussed below). As an example,the calibrant 1418 could be a saline solution. The microprobe 1302 couldbe tested and/or calibrated, by reading the pH of the saline solution,before the microprobe 1302 is located in the tissue to be measured. Ifthe microprobe 1302 being tested outputs erroneous data, the microprobe1302 can be discarded, and another microprobe 1302 can be used. If themicroprobe 1302 contains a plurality of sensing elements, the circuitry1412 may employ built-in test algorithms to reconfigure the microprobe1302 to utilize correctly functioning sensing elements followingcalibration.

In an alternative example of the microsensor system 1300, a plurality ofmicroprobes 1302 are provided, and the plurality of microprobes 1302 areutilized to simultaneously gather information from different locationsof the tissue being measured.

FIG. 15 is a schematic diagram showing an example of electricalconnections to the source 1504 and drain 1506 of the ISFET 1406, and tothe reference electrode 1410. The reference electrode 1410 is operablycoupled to the gate insulator 1505 of ISFET 1406 via space that isexterior to the microprobe 1302. Ammeter 1508 is connected to the drain1506, and resistor 1512 is connected in series with the ammeter 1508.Variable voltage source 1510 is connected to the source 1504, andvariable voltage source 1514 is connected to the reference electrode1410. Thus, a means 1508 for measuring the drain-source current throughresistance 1512, the voltage source 1514 for biasing the gate and avoltage source 1510 for biasing the drain-source voltage complete theexemplary circuit.

B2. Microprobe Delivery System

The microsensor system 1300 may also include a microprobe deliverysystem 1303 (shown in FIG. 13) for delivering the microprobe 1302 to thedesired tissue for assessment. For example, the microprobe deliverysystem 1303 could deliver the microprobe 1302 manually, pneumatically,or electrically, or using a magnetic, electromagnetic, electric,piezoelectric or electrostatic actuator, or some other type ofmechanical actuator. As an example, the microprobe 1302 could be firedfrom a micromachined delivery system 1303 that is electricallycontrolled by the control module 1304. A microelectromechanical system(MEMS) may be used to implement the microprobe delivery system 1303.MEMS comprises using microelectronic processing techniques tomanufacture mechanical devices. For example, a piezoelectric,electrostatic, or electromagnetic actuator, for delivering themicroprobe 1302 to the desired location in the dermis, could bemonolithically fabricated with the ISFET 1406 and the referenceelectrode 1410 (and with additional circuitry 1412 if additionalcircuitry 1412 is included).

In the example shown in FIG. 16, the actuator 1602 of the microprobedelivery system 1303 causes a cantilever arm 1604 that the microprobe1302 is attached to, to move from a first position near a base or pad1606, to a second position cantilevered from the first position, inwhich the microprobe 1302 is inserted into the desired location in thedermis 1608. The microprobe 1302 could be designed to break off from thecantilever arm 1604 once positioned in the dermis 1608. In anotherexample, the actuator could be configured to fire the microprobe 1302like a projectile, into the dermis 1608.

In an alternative example shown in FIG. 17, the microprobe deliverysystem 1702 has a plurality of actuators 1704, attached to a pad 1706,and is configured to deliver a plurality of microprobes 1302 intotissue, either one at a time, in groups, or simultaneously. Eachmicroprobe 1302 may correspond with an actuator, or additionalmicroprobes 1302 or actuators could be included. As an example, the pad1706 could be flexible to facilitate placement against skin. Examples ofmethods for fabricating flexible circuitry and related microelectronicdevices are found in pending U.S. patent application Ser. No. 10/313,552(NC#79797), filed 6 Dec. 2002, titled “Flexible Display Apparatus andMethod”, which is incorporated herein by reference. The microprobes 1302could be placed against the skin of a patient by placing the pad 1706against the skin. The actuators 1704 in the microprobe delivery system1702 could then be activated to deliver the one or more of the pluralityof microprobes 1302 into the dermis of a patient, either one at a time,in groups, or simultaneously. Alternatively, rather than providing a pad1706 having an array of actuators 1704 and microprobes 1302, a pluralityof microsensors 1302 could be provided individually, and could beindividually loaded into individual delivery systems, for delivery intothe dermis, either one at a time, in groups, or simultaneously. Themicroprobe delivery system 1702 is not limited to use with pH sensors,and the microprobes 1302 discussed herein are not limited to being pHsensors. The microprobe delivery system 1702 may be used with any typeof microprobe, or group or array of microprobes, for measuring, sensing,or reading data, or for delivering substances or electrical impulses, ator near the skin surface. For example microprobes could be used forsensing temperature, chemical or other biological characteristics or theskin, or could be used for applying electrical impulses. Generally, themicroprobes discussed herein could be used for any of these, oradditional applications.

B3. Control Module

The control module 1304, shown in FIG. 18, receives, processes, andpresents (or outputs) information regarding data measured by themicroprobe 1302. The example of the control module shown in FIG. 18includes a processor 1802 (which for example, may be a microprocessor),a main memory 1804 (which for example, may be RAM), a nonvolatile memory1806 (which for example, may be an optical disc drive, a magnetic diskdrive, a tape drive, or any other suitable storage device), an input1808 (which may be an interface for receiving data from one or moremicroprobes 1302). The control module 1304 may also have an output 1810,which could be coupled to the display 1812 to output information to thedisplay 1812, and which could also output information to a printer, anetwork, an alarm, and/or a data archiving device. In some examples, theoutput 1810 could output information to a patient status data processor12 (shown in FIG. 1). The main memory 1804 and/or the nonvolatile memory1806 may be used to store programming instructions executed by theprocessor 1802. The control module 1304 may also include a flat paneldisplay 1812 for presenting information, and a user interface 1814(which for example, may include buttons and/or knobs and/or atouchscreen, and which could include a keyboard or keypad). The controlmodule 1304 may also have circuitry, and/or may run algorithms, foron-site calibration and/or testing, of the ISFET microsensor 1406 in themicroprobe 1302. The control module 1304 may be configured to be handheld, or could be larger or smaller. In alternative examples the controlmodule 1304 could be implemented in a personal computer, a workstation,a mainframe computer, a supercomputer or could be networked across aplurality of devices. Power for the control module 1304 may be suppliedexternally from the control module 1304, or from within the controlmodule 1304, with for example, a battery, photovoltaic, chemical,radioisotope and/or kinematic electrical generator. An example of asuitable micro power device can be found in pending U.S. patentapplication Ser. No. 10/683,248 (NC#95867), filed 10 Oct. 2003, titled“Micro-Power Source”, which is incorporated herein by reference.

The control module 1304 may be configured to output any combination ofdata and/or assessments. For example, the control module 1304 maypresent information such as tissue pH and/or temperature, as well asassessments of trends, shifts, variance, and trends in the variance, inthe pH and/or temperature. The control module 1304 could further providea shock assessment index and/or triage index, and could also presentinformation concerning the probability of the onset of shock or death,based on appropriate algorithms. Blood flow, and/or blood gasinformation could also be presented by the control module 1304. Thecontrol module 1304 could also perform calculations and provideadditional statistical analysis information, such as described belowwith regard to the patient status monitor 10, and/or described in therelated applications referenced above, and/or described in U.S. Pat. No.5,671,734, issued Sep. 30, 1997, titled “Automatic Medical SignMonitor”, which is incorporated herein by reference. In some examples,some or all of the functions of the control module 1304 could beimplemented by the patient status data processor 12, described above.

B4. ISFET Structure

FIG. 19 is a cross sectional view of an example of the ISFET sensor1406, which may be used in the microprobe 1302. The ISFET 1406 may befabricated in a silicon layer 1902, which may be a portion of a bulksilicon wafer, or which may be a thin silicon layer on an insulatingsubstrate, for example, when a silicon-on-sapphire (SOS) orsilicon-on-insulator (SOI) wafer is used. Silicon-on-sapphire (SOS) andsilicon-on-insulator (SOI) may permit using simpler packaging and mayprovide improved device performance and encapsulating advantages, incomparison to bulk silicon. Passive or active wireless communicationscircuitry could be readily integrated onto SOI and SOS substratesadjacent to the ISFET 1406. SOS may provide lower power, fasteroperation (which facilitates running real time algorithms in associatedcircuitry), and may be particularly beneficial for wirelesscommunications. Sapphire has reduced toxicity and generally is morebiocompatible with the human body than silicon. Exemplary methods forforming circuitry in SOS are described in pending U.S. patentapplication Ser. No. 10/614,426 (NC#84892), issued 7 Jul. 2003, titled“Silicon-on-Sapphire Display Apparatus and Method of Fabricating Same,”which is incorporated herein by reference.

The ISFET has a source portion 1504 and a drain 1506 portion, which maybe formed by incorporation of dopants into the silicon layer 1902. As anexample, boron (p-type) dopants may be incorporated into n-type siliconlayer 1902 by ion implantation and subsequent thermal annealing orsimilar techniques as practiced in the art of microelectronicfabrication, to form the source portion 1504 and the drain portion 1506.A gate insulator 1908 may be formed on the silicon layer 1902 toestablish a channel portion 1910, which is defined as the predominantregion of current flow when the ISFET 1406 is biased in the on state.The gate insulator 1908 may be formed in a single step or in a pluralityof steps. As an example, the gate insulator 1908 may be formed by hightemperature oxidation of the silicon to form stoichiometric silicondioxide (SiO₂). This may be followed by the high temperature depositionof silicon nitride (Si₃N₄) by the pyrolysis of ammonia anddichlorosilane. Contact 1912 provides electrical contact to the source1504, and contact 1914 provides electrical contact to the drain 1506. Asan example, the contacts 1912, 1914 may be aluminum or an aluminumalloy, for example 99% Al-1% Si, or may comprise titanium-silicide orother silicides, or other materials used in the art of microelectronicfabrication. Encapsulant 1402 is formed to hermetically seal the ISFETmicrosensor 1406, except for the active gate region 1908 (and thereference electrode 1410) shown in FIG. 14, which are exposed to theenvironment. As an example, the environment could be human or animaltissue. As an example, the encapsulant 1402 may be an epoxy materialthat can withstand sterilization procedures required for the microprobe1302.

B5. Microsensor System Fabrication

FIGS. 20A-C are a flowchart of an operational sequence 2000 forfabricating a microsensor system 1300 that includes a microprobe 1302that has a monolithically fabricated ISFET sensor 1406, referenceelectrode 1410, and associated circuitry 1412, in accordance with anexemplary embodiment. In other examples of the sequence 2000, one ormore of the operations may be omitted. The sequence includes operation2002, which comprises providing a semiconductor wafer, which may forexample, be bulk silicon, SOS, SOI, or other suitable material. As anexample, a p-type bulk silicon wafer may be used. In operation 2004, ann-well region is formed in the semiconductor wafer, to define asemiconductor layer wherein the ISFET microsensor 1406, referenceelectrode 1410, and associated circuitry 1412 are fabricated. As anexample, the n-well region may be formed by thermal oxidation,photolithographic patterning, and etching of the oxide. N-type dopantsmay be added, by using, for example, ion implantation of phosphorousfollowed by furnace oxidation and thermal treatment to establish thedesired well depth (or drive). In the case of SOS, additional operationsare employed to form a suitable semiconductor layer as described inco-pending U.S. patent application Ser. No. 10/614,426, which ispreviously incorporated herein.

In operation 2006, the gate insulator 1908 may be formed by acombination of growth or deposition steps as desired. As an example, asilicon layer may be thermally oxidized by heating the silicon layer inan oxygen ambient environment to form silicon dioxide. Subsequently, asilicon nitride layer may be deposited on the silicon dioxide to formthe gate insulator 1908. In operation 2008, the gate insulator 1908 maybe patterned, if desired, by photolithographic and etching steps knownin the art of microfabrication. Patterning and etching the gateinsulator 1908 provides a means for electrical contact with the source1504 and drain 1506 regions (also called portions) that are subsequentlyformed, and also defines regions used by the associated circuitry (whichmay be included).

In operation 2010, the field, source 1504, and drain 1506 regions of theISFET 1406 may be formed by incorporating dopants into the siliconlayer, for example, by using photolithographic patterning, ionimplantation, and furnace oxidation and drive. If desired, in operation2012, dopants may be simultaneously incorporated into associatedcircuitry 1412 (which may be included, and as an example, may includediode circuitry). In operation 2014, a contact layer may be formed bydeposition of aluminum or an aluminum alloy, using techniques known inthe art, for example, by sputtering on an appropriate target. Inoperation 2016, the contact metal may be patterned to form contacts1912, 1914 during photolithographic and etching steps which are known inthe art of microfabrication. The reference electrode 1410 may bemonolithically fabricated near the ISFET sensor 1406. As an example, thereference electrode 1410 may be fabricated using lift-off techniquesknown in the art as described in operations 2018, 2020, and 2022. Forexample, in operation 2018 the reference electrode material 1410 may beformed on a photoresist layer, by using deposition, spin-casting orrelated techniques as practiced in the art. As an example, the referenceelectrode material in operation 2018 may be silver, and operation 2018may comprise depositing silver on the photoresist. In operation 2020,the silver may be reacted in solution to form silver chloride on thesilver layer. In operation 2022, the underlying photoresist is desolvedto remove the unwanted silver/silver-chloride layer, thereby producing asilver/silver-chloride reference electrode 1410 in the desiredconfiguration, which may be described as developing the underlyingphotoresist to form the reference electrode 1410. Variations in devicefabrication will be readily apparent to persons skilled in the art ofsemiconductor processing. By example, these may include such variationsas to move operation 2012 to a point between operation 2004 andoperation 2006 if the desired device fabrication sequence warrants it.

In some examples, a plurality of ISFETS 1406, reference electrodes 1410,and (in some embodiments) associated circuitry 1412, may bemonolithically integrated on a single wafer. In operation 2024, thewafer containing the fabricated ISFET 1406, the reference electrode1410, and associated circuitry 1412 may be diced or cleaved intoindividual die comprising functional units. In operation 2026, wirebondconnections may be made to the ISFET 1406, reference electrode 1410, andassociated circuitry 1412, using techniques known in the art. Inoperation 2028, the encapsulant housing 1402 may be formed around eachdie, respectively, to hermetically seal the die (except for the areawithin the aperture 1404). The aperture 1404 is formed in theencapsulant 1402 to permit contact between the tissue to be measured,and at least a portion (and typically all) of the gate 1408 of the ISFET1406, and at least a portion (and typically all) of the referenceelectrode 1410, or to permit fluid communication between a fluid in thetissue, for example blood, and at least a portion of the gate 1408, andat least a portion of the reference electrode 1410.

In operation 2030, a microprobe delivery system 1303, 1702 is provided,for delivering the microprobe 1302 (or a plurality of microprobes 1302),into tissue. In operation 2032, calibrant 1418 is also provided, whichmay be located within the microprobe 1302, or the delivery system 1303,1702. In operation 2034, the microprobe 1302 is positioned in thedelivery system 1303 (or a plurality of microprobes 1302 are positionedin the delivery system 1702). In operation 2036, the microsensorassembly including the microprobe 1302, calibrant 1418, and deliverysystem 1303, 1702, is then sterilized using any suitable technique, forexample, by heating in an autoclave or by utilizing high radiation dosesfrom a radioactive source. In operation 2038, the microsensor assemblywhich includes the microprobe 1302, calibrant 1418, and delivery system1303, 1702, is packaged. In operation 2040, a control module 1304 isprovided, and in operation 2042, the microprobe 1302 is operablyconnected to the control module 1304, for example with wires 1306.

IV. Operation

In addition to the various hardware embodiments described above, adifferent embodiment concerns a method for monitoring data, which insome examples may be applied to measuring and/or monitoring one or morecharacteristics of tissue. Tissue pH is an example of a characteristicof the tissue that may be measured and/or monitored. In the examplesbelow, “patient” status may additionally be more generally understood asthe status of the system that is being monitored, which may include, forexample, a manufacturing environment and the data produced from such forquality control, the stock market or other economic systems, or apopulation group (which may include plants, bacteria, etc.) beingmonitored for disease or exposure to agents injurious to their or itshealth.

A. Signal-Bearing Media

In the context of FIG. 1 or FIG. 13, the method for monitoring data(which in some examples may be embodied as a method for monitoringpatient status, or as a method for measuring and/or monitoring acharacteristic of tissue), may be implemented for example, by operatingthe computer to execute a sequence of machine-readable instructions,which can also be referred to as code. These instructions may reside invarious types of signal-bearing media. In this respect, one embodimentconcerns a programmed product, comprising a signal-bearing medium orsignal-bearing media tangibly embodying a program of machine-readableinstructions executable by a digital processing apparatus to perform amethod for monitoring data, which in some examples may be applied tomeasuring/monitoring patient status and/or a characteristic of tissue.

The signal-bearing medium may comprise, for example, the memory 28and/or the nonvolatile storage 30 in the patient status data processor12. Alternatively (or in addition), the signal-bearing medium maycomprise, for example, the main memory 1804 and/or the nonvolatilememory 1806 in the control module 1304. Alternatively, the instructionsmay be embodied in a signal-bearing medium such as the optical datastorage disc 500 shown in FIG. 5. The optical disc can be any type ofsignal bearing disc, for example, a CD-ROM, CD-R, CD-RW, WORM, DVD-R,DVD+R, DVD-RW, or DVD+RW. Whether contained in the patient status dataprocessor 12, the control module 1304, or elsewhere, the instructionsmay be stored on any of a variety of machine-readable data storagemediums or media, which may include, for example, direct access storage(such as a conventional “hard drive”, a RAID array, or a RAMAC), amagnetic data storage diskette (such as a floppy disk), magnetic tape,digital optical tape, RAM, ROM, EPROM, EEPROM, flash memory,magneto-optical storage, paper punch cards, or any other suitablesignal-bearing media including transmission media such as digital and/oranalog communications links, which may be electrical, optical, and/orwireless. As an example, the machine-readable instructions may comprisesoftware object code, compiled from a language such as “C++”.

B1. Overall Sequence of Operation of Patient Status Monitor (Which Maybe Used in Conjunction with Microprobe or Microprobe System)

In carrying out a method embodiment, the patient status data processor12, shown in FIG. 1, may flag points above and below simple normal valuethresholds, determine shifts in the mean, and identify outlier and burstpoints of, for example, a medical sign or a lab test value. The patientstatus data processor 12 applies the Dynamic Change point Detection(DCD) algorithm to detect a change in a patient's health state (or achange in a characteristic of tissue), to detect changes toward or awayfrom a patient's reference value range, and to detect when no change hasoccurred when one should have occurred, and also employs a simplethreshold test to flag values outside the patient's threshold valuerange. If a value is outside the patient's threshold value range or if achange in the patient's health status (or of the tissue) is indicated,then the patient status data processor 12 may display the status on adisplay, and/or may print a log of the patient's health status orotherwise activate an alarm status indicator or actuator. The actuatoris envisioned to automatically take remedial action in response to saidalarm.

The Dynamic Change point Detection algorithm is a type of trendanalysis, and is used to study changes that occur in a data set overtime. Using the Dynamic Change point Detection algorithm facilitatesearly detection of changes, which permits intervention before a problemcauses irreversible damage. The problem could be a condition that slowlydegrades over time or the problem could be a shift in the value of amedical sign or lab test value.

The Dynamic Change point Detection is a process that performs any one ormore of the following:

-   1. Allows the user to exclude data points from the analysis.-   2. Allows the user to change the thresholds used by one or more of    the following twelve statistics. One or more of these statistics may    be used in the DCD detection process. The twelve statistics are:    four Shewhart test statistics, two Maximum CUSUM statistics, two    Fast Initial Response CUSUM statistics, a statistical method for    calculating the mean and standard deviation from the incoming data    set, a statistical method for flagging and removing outliers from    the mean and standard deviation estimates, a method for comparing    the datapoint to a patient reference value, a method for comparing    the trend direction to the patient reference value range, a method    for determining a change in value is over due, and a method for    determining when the mean and standard deviation should be reset and    subsequently resetting the mean and standard deviation when    appropriate.-   3. Can set the mean and standard deviation used in the analysis from    the incoming data or can use mean and standard deviation values    supplied by a user or a normal value reference.-   4. Can determine the mean and standard deviation from a small    sample. While the Dynamic Change point Detection process is looking    for 6 to 8 consistent data points, outliers are flagged and removed    if their removal would not cause the standard deviation to be zero.-   5. Allows the user to specify the number of consistent data points    to use in calculating the mean and standard deviation.-   6. Two state Markov's are initiated on the first data point. Hence    the Dynamic Change point Detection process provides information on    the current data point relative to the current mean and standard    deviation.-   7. Detects upward and downward trends, and upward and downward    shifts (An upward trend is a set of datapoints whose value is    increasing over time, and a downward trend is a set of datapoints    whose value is decreasing over time. An upward shift occurs when the    mean of the data set increases and subsequent data points occur    around that mean. A downward shift occurs when the mean of the data    set decreases and subsequent data points occur around that mean.),-   8. Detects outliers (points two or more standard deviations from the    mean),-   9. Detects burst points (points three or more standard deviations    from the mean),-   10. Can reset the estimated mean and standard deviation if the mean    in use is found to be significantly higher or lower than the    incoming data.-   11. Resets that change point detection statistics when shifts or    trends are detected.-   12. Estimates the starting and ending points of a change in the    medical sign or lab test value,-   13. Estimates the curve associated with the change in the medical    sign or lab test value using polynomial regression,-   14. Forecasts the medical sign or lab test value,-   15. Calculates a confidence interval for the forecast medical sign    or lab test value, and-   16. Color codes detected changes. No change detected is flagged    green, shifts or trends detected by the two state Markov's are    flagged yellow, statistically significant shifts or trends are    flagged red, outliers are flagged red, and bursts are flagged black.

FIGS. 6A-C are a flow chart showing a sequence 600 for monitoringpatient status in accordance with an example of a method embodiment. Forease of explanation, but without any intended limitation, the example ofFIGS. 6A-C is described in the context of the patient status monitor 10and the microsensor system 1300 described above. As an example, thesequence 600 may be used for detecting the onset of a disease or achange in the health status of an individual (who may also be called apatient), or for detecting the onset of shock, or for detecting whethertissue may become non-viable.

Although sequence 600 primarily seems to illustrate a method formonitoring the status of a single medical patient's status, manyembodiments of the method aspect of the invention are generallyapplicable to monitoring data in any type of application. For example,the method for monitoring data could be used in any quality controlmanufacturing situation where there is a preferred reference rangespecification, a set of population values associated with the currentmanufacturing process and a number of interventions that might be doneto move a process into the preferred reference range such as producingbaked goods, manufacturing parts, and microchip clean room temperatureand air quality particulate monitoring.

Further examples where the method may be applied include any populationmedical surveillance situation that includes monitoring the rates ofoccurrence or counts of grouping of ICD-9 codes, a single ICD-9 code,and/or signs and symptoms where these rates and counts represent therates or counts of disease or biological exposure, injury, chemicalexposure, radiological exposure, nuclear exposure, or other generalhealth related items.

Other examples include any patient medical monitoring situation thatincludes monitoring signs and symptoms associated with an individualsuch as pulse rate, oxygenation, systolic and diastolic blood pressure,temperature, weight, tissue PH, breathing rate, EEG, chemical/hormelevels, or any other signs, symptoms, or lab test values.

In other examples the method could also be applied to the stock marketto watch the movement of stocks, bonds, treasury notes, money market,etc. movement toward or away from a buy or sell reference range. Itcould also be applied to the housing market.

The method could also be applied to the monitoring of any economicsituation where there is a current value and a reference range such asthe State of California's deficit current value, the interventions suchas removal of the vehicle tax increase, and the preferred range oflittle or no deficit.

The specific example of the sequence 600 will now be discussed. Thesequence 600, which may be performed by the patient status dataprocessor 12, may include and may begin with operation 602, whichcomprises initializing process values.

The sequence 600 may also include operation 604, which comprisesreceiving medical intervention information.

The sequence 600 may also include operation 605, which comprisesreceiving patient reference values (which may be called patient specifictissue reference values), and patient threshold values (which may becalled user input thresholds). As an example, the patient and/or tissuereference values may be determined by the patient's doctor. Upper andlower thresholds may be used in the analysis of the patient's status:Alternatively, or in addition to receiving patient reference values,normal population reference values may be inputted from a libraryreference table for the sign or lab test data type.

The sequence 600 may also include operation 606, which comprisesreceiving a data point for a data type. As an example, the data type maybe tissue pH. Operation 606 may also include receiving an identifier ofthe data type. The data set may represent, for example, medical signs orlab data of an individual. One or more data collection devices 14, 16,18 may provide the data that is input in operation 606. The data pointmay be designated x₁, and may be from a data set comprised of a timeseries of x₁, . . . , x_(m). In this example, “1” is the index of thefirst data point, and m is a positive integer that represents the numberof data points that have been input and received into the process.

The sequence 600 may also include operation 608, which compriseschecking validity of the data point. The validity of the data point maybe based on user criteria. Data points that are determined to be invalidare removed from further consideration, in operation 610.

The patient status monitor 10 collects (or receives) a time ordered setof medical sign, and lab test data and either uses user-specified meanand standard deviation values or determines the mean, and standarddeviation from the incoming data. Accordingly, the sequence 600 may alsoinclude operation 612, which comprises determining if a baseline meanand baseline standard deviation have been obtained for the data type. Ifa baseline mean and baseline standard deviation have not been obtained,then operation 614 is performed, which comprises obtaining a baselinemean and a baseline standard deviation. The user may specify that one ormore data points not be used in the analysis, set the number of pointsused in calculating the mean and standard deviation, and change thethreshold settings on the various statistics.

The mean and standard deviation may be calculated from the incomingdata. Alternatively, the operation of obtaining a baseline mean and abaseline standard deviation may comprise using population values of themean and standard deviation. In another alternative, the operation ofobtaining a baseline mean and a baseline standard deviation may compriseusing preset background values of the mean and standard deviation.

The mean and standard deviation may be derived from the currentlyavailable data points x₁, . . . , x_(n) that have been inputted, or maybe derived from a baseline data set x_(K), . . . , x_(L) identifiedduring a baseline reset. “n” is defined as the current number of datapoints entered if the baseline mean and standard deviation have not beenset, and is the last data point used to create an acceptable baselinemean and standard deviation after the mean and standard deviation areset for the first time. (The letter “m” is sometimes used instead of theletter “n”.) The mean and standard deviation may be, when needed,calculated and reset a number of times. During the operation 614 ofobtaining the baseline mean and standard deviation, non-conforming datapoints may be identified and removed from the baseline data set. Avariable “r” is set equal to the number of data points removed fromconsideration either a priori by the user or because they are found tobe non-conforming data points. When the mean and standard deviation arecalculated from the incoming data, four Shewhart tests are used toremove and flag outliers in the mean and standard deviationcalculations. A point that is three standard deviations or more from themean is flagged as a burst point. A point that is between two and threestandard deviations from the mean is flagged as an outlier point. Burstand outlier data points are removed from the calculations if theirremoval does not cause the standard deviation to be zero. The process iscontinued until the standard deviation is non-zero and at least sevenpoints that meet these criteria are found. The resultant mean andstandard deviation are used in the signal detection tests. An adjustmentmay be made in the non-conforming data point removal when the data isdiscrete instead of continuous.

In following description of operations performed to obtain a baselinemean and a baseline standard deviation, a first data point received inoperation 606 may be referred to as the first data point, and a seconddata point received in operation 606 may be called a second data point.More specifically, the operation 614 of obtaining a baseline mean andbaseline standard deviation may comprise:

for the first data point, setting the mean equal to the value of thefirst data point, and setting the standard deviation equal to zero;

for the second data point, setting the mean equal to the average of thefirst and second data points, and setting the standard deviation equalto zero;

for data points received after the first and second data points,calculating the mean using the formula:

${mean} = \frac{\sum\limits_{i = 1}^{m}\;{R\left( x_{i} \right)}}{m - r}$

and for data points received after the first and second data points,calculating the standard deviation using the formula:

${sd} = \sqrt{\frac{\sum\limits_{i = 1}^{m}\;\left( {{R\left( x_{i} \right)} - {mean}} \right)^{2}}{m - r - 1}}$

wherein

R(x_(i)) removes x_(i) from the calculation if

${\sqrt{\frac{i - r}{i - r + 1}}{\frac{X_{i} - {mean}}{sd}}}\underset{\_}{>}2$or if the user has specified that x_(i) is to be removed; and is equalto x_(i), otherwise.but wherein x_(i) is not removed from the calculation if settingR(x_(i))=remove data point i results in the standard deviation beingequal to zero; (In other words, if removal of a data point will causethe standard deviation to be zero, the data point is retained.);

and wherein r is equal to the number of data points removed from themean and standard deviation calculations; and

wherein the above sequence of operations for calculating the mean andthe standard deviation is repeated until the standard deviation is notequal to zero, and until at least seven conforming data points have beenfound.

The sequence 600 may also include operation 616, which comprisescalculating test statistics. As an example, the operation of calculatingtest statistics may comprise applying two MAX CUSUM tests (MAXimumCUmulative SUM) to calculate an upper MAX CUSUM value and a lower MAXCUSUM value, and/or applying two FIR MAX CUSUM tests (Fast InitialResponse CUmulative SUM) to calculate an upper FIR MAX CUSUM value and alower FIR MAX CUSUM value, and/or performing four Shewhart statisticaltests, and/or calculating an approximate standard score, and/orcalculating signal length, and/or calculating signal amplitude or shift.The Dynamic Change point Detection processor may apply small sampleversions of the statistical tests. The statistical tests are conductedon the data to detect changes in the patient's health state. If a changein a patient's sign or lab test data is indicated, then the patient'ssign or lab test data health status statistics may be presented on adisplay.

Statistical methods used for medical monitoring may be categorized intwo different categories. The first category includes statisticalmethods that are concerned with detecting sharp changes which are starkdepartures from historic levels, and that often, rapidly, return to thenorm, and which are called a burst or outlier. Moving average charts andproportion charts are examples of the first type of statistical method.The second category includes statistical methods that are concerned withsmaller deviations from historic levels that are sustained over anextended period of time, and which are known as a shift or trend. Linearregression and Cumulative Sums (CUSUM) are examples of the second typeof statistic. Additionally, two-state Markov statistics such as theCUSUM statistic are useful for detecting shifts and trends in data, andcan provide an estimate of the beginning and end of a signal. Foursmall-sample two-state Markov statistics may be used to detect trendsand shifts in the data and to determine the beginning and end of asignal. The detection and removal of normal cyclic variation could beadded to these algorithms to remove artifacts that may produce falsealarms and therefore improve the detection of clinically significantevents. Alternative embodiments could also include operations forforecasting the value of the data type for some time in the future usingmethods known to the art such as polynomial regression line fit.Additionally, alternative embodiments could include operations forreconstructing the signal present in data points (such as data points Kthrough L discussed below).

The statistical tests will now be discussed in more detail. As mentionedabove, the approximate standard score is defined as follows:

${\hat{t}}_{i} = {\left( \overset{\_}{\omega} \right)\left( \frac{X_{i} - {mean}}{sd} \right)}$where ω is the approximate ratio of the standard normal threshold andthe t-test threshold associated with the degrees of freedom in the meanand standard deviation calculations. In an alternative embodiment theapproximation is replaced with the actual ratio.

$\overset{\_}{\omega} = \sqrt{\frac{n - r}{n - r + 1}}$

CUSUMs will now be discussed in more detail. Let β=a constant, forexample 0.5, and H=the threshold used in the CUSUM statistic tests,where H is a constant chosen such that the reciprocal of the average runlength equals the desired probability of false alarm.

The CUSUM upper sum isSH _(i)=max(SH _(i−1) +{circumflex over (t)} _(i)−β,0)and the MAX CUSUM upper sum is

${{SH}\;\max_{j}} = {\max\limits_{{i = 1},{\ldots\mspace{20mu} j}}\left( {SH}_{i} \right)}$where SH₀=0.The CUSUM lower sum isSH _(i)=max(SH _(i−1) −{circumflex over (t)} _(i)−β,0)and the MAX CUSUM lower sum is

${{SL}\;\max_{\; j}} = {\max\limits_{{i = 1},{\ldots\; j}}\left( {SL}_{i} \right)}$where SL₀=0.The FIR CUSUM upper sum isFIRSH_(i)=max(FIRSH_(i−1) +{circumflex over (t)} _(i)−β,0)and the FIR MAX CUSUM upper sum is

${{FIRSH}\;\max_{j}} = {\max\limits_{{i = 1},{\ldots\; j}}\left( {FIRSH}_{i} \right)}$where FIRSH₀=H/2 where H the threshold used in the CUSUM statistictests.The FIR CUSUM lower sum isFIRSL_(i)=max(FIRSL_(i−1) −{circumflex over (t)} _(i)−β,0) andand the FIR MAX CUSUM lower sum is

${{FIRSL}\;\max_{j}} = {\max\limits_{{i = 1},{\ldots\; j}}\left( {FIRSL}_{i} \right)}$where FIRSL₀=H/2 where H the threshold used in the CUSUM statistictests.

The length of the signal, the index of the signal starting point, andthe data point index of the signal ending point may be estimated asfollows: The upper signal length equals zero if the CUSUM upper sum isequal to zero, otherwise one is added to the upper signal length. If theMAX CUSUM upper sum is constant three times in a row, reset the CUSUMupper sum and the MAX CUSUM upper sum. The starting point in the signalis the index of the data point where the signal length is one, K, andthe ending point in the signal is the index of the data point where theMAX CUSUM upper sum reached its maximum value, L. The lower signallength equals zero if the CUSUM lower sum is equal to zero, otherwiseone is added to the lower signal length. If the MAX CUSUM lower sum isconstant three times in a row, reset the CUSUM lower sum, and the MAXCUSUM lower sum. The starting point in the signal is the index of thedata point where the signal length is one and the ending point in thesignal is the index of the data point where the MAX CUSUM lower sumreached its maximum value.

The positive signal amplitude or upward shift for the ith data point isestimated as follows:

${shift}_{i,{upper}} = {\left( {\frac{{SH}_{i}}{{length}_{i,{upper}}} + \beta} \right)\left( \overset{\_}{\omega} \right)({sd})}$

The negative signal amplitude or downward shift for the ith data pointis estimated as follows:

${shift}_{i,{lower}} = {\left( {\frac{{SH}_{i}}{{length}_{i,{lower}}} + \beta} \right)\left( \overset{\_}{\omega} \right)({sd})}$

The fast initial response (FIR) maximum cumulative sum (MAX CUSUM)algorithm and the maximum cumulative sum (MAX CUSUM) algorithm assumethat the data is normally distributed around some normal value. Generalpopulation normal values for each medical sign and lab test value may becontained in a normal reference. These values may be used in thecalculations instead of the estimates based on the incoming data.

The mathematical derivation of the process described above is asfollows. It is assumed that a set of time ordered random variables x₁, .. . , x_(m) of the medical sign or lab test values is normallydistributed around the two known states F and G. F and G define a twostate Markov process, where the first state F is associated with x₁, . .. , x_(k) and x_(L+1), . . . , x_(m) and the second state G isassociated with x_(K+1), . . . , x_(L). To determine the point K wherethe data changes from state F to G, and the point L where the datachanges from state G to F, let

$S_{m} = {{\max\left( {{S_{m - 1} + {\ln\left( \frac{g\left( x_{m} \right)}{f\left( x_{m} \right)} \right)}},0} \right)} - {\left( \frac{1}{m} \right){\min\limits_{L\underset{\_}{<}m}{\sum\limits_{j = {L = 1}}^{k}\;{\ln\left( \frac{g\left( x_{j} \right)}{f\left( x_{j} \right)} \right)}}}}}$where L and k are chosen to maximize S_(m). A change in Markov statefrom F to G occurs when S_(m) is greater than H where H is a constantchosen such that the reciprocal of the average run length equals thedesired probability of false alarm. The probability of a false alarm isapproximately 0.05 for the two-state Markov process implemented. In theimplemented process, a change in Markov state from G to F is assumed tohave occurred when S_(m+1),S_(m+2), and S_(m+3) are less than S_(m).

The maximum value of S_(m) may be calculated recursively according to:

$S_{j} = {\max\left( {{S_{j - 1} + {\ln\left( \frac{g\left( x_{j} \right)}{f\left( x_{j} \right)} \right)}},0} \right)}$

${S\;\max_{m}} = {\max\limits_{{j = 1},{\ldots\; m}}\left( S_{j} \right)}$In the FIR implementation of the maximum cumulative sum (MAX CUSUM),S₀=H/2 and in the maximum cumulative sum S₀=0.0.

If f(x) is distributed N(μ₁,σ²) and g(x) is distributed N(μ₂,σ²) then ifμ₂≧μ₁

${{SH}_{j} = {\max\left( {{{SH}_{j - 1} + \frac{x_{j} - \mu_{1}}{\sigma} - \frac{\mu_{2}}{2\;\sigma}},0} \right)}},$and

${{SH}\;\max_{m}} = {\max\limits_{{j = 1},{\ldots\; m}}\left( {SH}_{j} \right)}$if μ₂≦μ₁

${{SL}_{j} = {\max\left( {{{SL}_{j - 1} - \frac{x_{j} - \mu_{1}}{\sigma} - \frac{\mu_{2}}{2\;\sigma}},0} \right)}},$and

${{SL}\;\max_{m}} = {\max\limits_{{j = 1},{\ldots\; m}}\left( {SL}_{j} \right)}$As an example, μ₂/(2σ) may be set to 0.5. The FIR implementation of theMAX CUSUM may be used to determine when a perceived state change isstatistically significant. The MAX CUSUM implementation may be used toestimate the number of data points belonging to the second state G.

The length of the second state may be estimated as follows: an estimateof L is the index of the maximum S_(j) value. To estimate k the processlooks backwards in time from {circumflex over (L)} for the most recentS_(j) value equal to zero. The associated index is an estimate of k.Therefore the estimated signal duration is {circumflex over(L)}−{circumflex over (k)} data points.

To reconstruct the signal belonging to the second state G, a polynomialregression curve may be used to fit the data points from {circumflexover (k)}+1 to {circumflex over (L)}. Models that determine when achange in a value is non-critical, such as the change in the systolicblood pressure when the patient sits up or rolls over in bed, could beadded.

Data points where the upper or lower MAX CUSUM value is greater thanzero may be marked yellow, and points where the upper or lower FIR MAXCUSUM value is greater than H may be marked red (for example, on aDynamic Change point Detection graph).

The sequence 600 may also include operation 618, which comprisesdetermining if the data point is non-conforming, and if so, flagging thecondition of the data point in operation 620 (which may compriseflagging outliers and burst points). Determining if the data point isnon-conforming in operation 618, and if so, flagging the condition ofthe data point in operation 620, may comprise the following: Calculatingthe approximate standard score using the formula:

${\hat{t}}_{i} = {\left( \overset{\_}{\omega} \right)\left( \frac{X_{i} - {mean}}{sd} \right)}$where ω is the approximate ratio of the standard normal threshold andthe t-test threshold associated with the degrees of freedom in the meanand standard deviation calculations. In an alternative embodiment theapproximation is replaced with the actual ratio.

$\overset{\_}{\omega} = \sqrt{\frac{n - r}{n - r + 1}}$

wherein n=the number of points used to estimate the mean and r is thenumber of data points that have been removed from the calculation;

but wherein t_(i) is set to zero if x_(i)−mean=0;

and wherein the data point is flagged as an outlier and is colored redon the Dynamic Change point Detection graph if t_(i) is greater than orequal to two and is less than three;

and wherein the data point is flagged as a burst point and is coloredblack on the Dynamic Change point Detection graph if t_(i) is greaterthan or equal to three.

When t_(i) is greater than or equal to 2, R(x_(i)) is set to remove theith data point, and the associated upward or downward count isincremented.

Operations 618 and 620 may alternatively be described as follows. FourShewhart tests may be conducted on the current data point to determineif the current data point is non-conforming and to flag its condition.Let δ and γ be test thresholds where γ>δ, for example, δ equals 2 and γequals 3. The four Shewhart non-conforming data flagging tests are asfollows:

A positive outlier is declared if:

${\sqrt{\frac{n - r}{n - r + 1}}\left( \frac{X_{i} - {mean}}{sd} \right)}\underset{\_}{>}\delta$A positive burst is declared if:

${\sqrt{\frac{n - r}{n - r + 1}}\left( \frac{X_{i} - {mean}}{sd} \right)}\underset{\_}{>}\gamma$A negative outlier is declared if

${\sqrt{\frac{n - r}{n - r + 1}}\left( \frac{X_{i} - {mean}}{sd} \right)}\underset{\_}{<}{- \delta}$And, a negative burst is declared if

${\sqrt{\frac{n - r}{n - r + 1}}\left( \frac{X_{i} - {mean}}{sd} \right)}\underset{\_}{<}{- \gamma}$

The number of data points in a major upward shift is determined bycounting the number of times in a row the calculation is ≧δ. The numberof data points in a major downward shift is determined by counting thenumber of times in a row the calculation is ≦−δ.

The sequence 600 may also include operation 622, which comprisesdetermining the direction of any signal present at the data point. Theoperation of determining the direction of any signal present at the datapoint may comprise the following statistical:

If the CUSUM upper sum, SH_(i)=max(SH_(i−1)+{circumflex over(t)}_(i)−β,0), is greater than the CUSUM lower sum,SL_(i)=max(SL_(i−1)−{circumflex over (t)}_(i)−β,0) the signal is abovethe current baseline mean and is declared (described) to be increasing.If the CUSUM upper sum, SH_(i)=max(SH_(i−1)+{circumflex over(t)}_(i)−β,0), is less than the CUSUM lower sum,SL_(i)=max(SL_(i−1)−{circumflex over (t)}_(i)−β,0), the signal is belowthe current baseline mean and is declared to be decreasing. If the CUSUMupper and lower sums are equal, the signal is declared to be neitherincreasing nor decreasing.

The sequence 600 may also include operation 624, which comprisesdetermining if a statistically significant positive or negative trend isoccurring, and may reset all of the CUSUM and FIR CUSUM values. IfFIRSH_(i)≧H a statistically significant positive trend is declared andif FIRSL_(i)≧H a statistically significant negative trend is declared.

The sequence 600 may also include operation 626, which comprises settinga flag, for example for triggering an alarm, if a statisticallysignificant positive or negative trend is occurring in a medicallyundesirable direction. The alarm may be triggered regardless of whetherthe data is within a threshold. As an example, the alarm may bepresented on a display.

The sequence 600 may also include determining if the data point isgreater than an upper patient (or tissue) reference value for the datatype in operation 628, and if so, flagging the data point as being abovethe normal range in operation 630. Similarly, the sequence 600 may alsoinclude determining if the data point is lower than a lower patient (ortissue) reference value for the data type in operation 632, and if so,flagging the data point as being below the normal range in operation634. The sequence 600 may also include operation 636, which comprisesflagging the data point as being within a normal range if the data pointis not greater than the upper patient reference value and is not lowerthan the lower patient reference value for the data type. The sequence600 may also include operation 638, which comprises conducting tests todetermine whether there is a trend in the patient's data away from thepatient reference range. The sequence 600 may also include operation640, which comprises triggering a warning alarm if there is a trend inthe patient's data away from the patient reference range. The sequence600 may also include operation 641, which comprises determining if anupper or lower trend test statistic is greater than a threshold (H), todetermine if a trend is statistically significant. If an upper or lowertrend test statistic is determined to be greater than the threshold (H)in operation 641, then the sequence 600 may also include operation 642,which comprises triggering a high level alarm. The sequence 600 may alsoinclude operation 644, which also comprises determining if an upper orlower trend test statistic is greater than a threshold (H). If an upperor lower trend test statistic is determined to be greater than thethreshold (H) in operation 644, indicating that movement has occurredtoward the reference value range, sequence 600 may also includeoperation 646, which comprises capturing this statistically significantevent that indicates an intervention is working.

The simple threshold test processor shown in FIG. 3 tests, flags, andcolor-codes patient medical sign and lab test values that are outsidewhat is considered normal for the patient (or for the population as awhole). Selected patient sign and lab test values within a selected daterange may be compared with patient threshold values. Values above andbelow the threshold values may be flagged in a table presentation andmay also generate an alarm. Also, the values may be presented in a bargraph presentation (such as a DCD analysis graph), wherein values arepresented over the time period selected, and values within the referencevalues are colored green, and values above or below the reference valuesare colored red. Black lines may also be drawn on the graph to displaythe upper and lower range for a sign or lab test value under review.Alternate annotations through use of symbology or color codes may alsobe employed by the user as desired, without limiting the scope of theteachings herein. A user may edit the patient reference values andpatient threshold values (or normal value references). The color-codingsystem may also mark the estimated start and end of potential events,thereby providing investigative information to the user.

The sequence 600 may also include operation 650, which comprisesdetermining if a downward or if an upward change is detected in inputdata. If no downward or upward change is detected in operation 650, thensequence 600 may also include operation 651, which comprises determiningif no upward or downward data change has been detected for a userspecified time (a no change time threshold) or longer, after anintervention has been implemented, and if so, the sequence 600 may alsoinclude operation 652, which comprises triggering a medium level alarm.The sequence 600 may also include operation 653, which comprisesdetermining if a lower or upper test statistic is greater than athreshold (H) (for example, determining if a lower FIR MAX CUSUM or ifan upper FIR MAX CUSUM is greater than H). The sequence 600 may alsoinclude operation 654, which comprises determining if the lower or upperMAX CUSUM indicates a signal {circumflex over (L)}−{circumflex over (k)}having a length greater than or equal to seven. If the determinations inoperations 650, 653, and 654 are all determined to be so, then thesequence 600 may also include operation 656, which comprises resettingthe mean and standard deviation using data points inputted since a lasttime the lower or upper MAX CUSUM value was zero. In other words, if adownward or upward change is detected in the input data, and if thelower or upper FIR MAX CUSUM is greater than H, and if the lower orupper MAX CUSUM indicates a signal {circumflex over (L)}−{circumflexover (k)} in length that is greater than or equal to seven, then themean and standard deviation may optionally be reset using the data sincethe last time the lower or upper MAX CUSUM value was zero. The followingequations may be used for resetting the mean and standard deviation inoperation 656:

${{mean} = \frac{\sum\limits_{i = {\hat{k} + 1}}^{\hat{L}}\;{R\left( X_{i} \right)}}{\hat{L} - \hat{k} - r}},$and

${sd} = \sqrt{\frac{\sum\limits_{i = {\hat{k} + 1}}^{\hat{L}}\;\left( {{R\left( X_{i} \right)} - {mean}} \right)^{2}}{\hat{L} - \hat{k} - r - 1}}$whereinR(x_(i)) removes x_(i) from the calculation if

${\sqrt{\frac{\hat{L} - \hat{k} - r}{\hat{L} - \hat{k} - r + 1}}{\frac{X_{i} - {mean}}{sd}}}\underset{\_}{>}2$or if the user has specified that x_(i) is to be removed; and is equalto x_(i), otherwise.

Generally, there are four potential reset conditions that may beimplemented. Conditions applied are dependent on the initialization bythe end-user.

Condition 1: If the signal is statistically significant, and containsenough data points to try a baseline reset, and the signal changedirection is toward the normal range, then the baseline should be resetand the information that the mean value has moved toward the normalrange is captured. Condition 1 may occur because the initial data pointsused in calculating the mean and standard deviation contained a signaland was not noise only data. Condition 1 may also indicate animprovement in the patient's status. For example a patient's drop inmean diastolic value from 105 to 100, which is still high, is animproved mean. If this is the case, it would be desirable to monitor fora continued decreased in diastolic value.

Condition 2: If the signal is statistically significant, and containsenough data points to try a baseline reset, and the signal changedirection is increasing and farther away from the normal range, then thebaseline should be reset and the information that the mean value hasincreased and is farther away from the normal range is captured.

Condition 3: If the signal is statistically significant, and containsenough data points to try a baseline reset, and the signal changedirection is decreasing and farther away from the normal range, then thebaseline should be reset and the information that the mean value hasdecreased and moved farther away from the normal range is captured.

Condition 4: If the signal is statistically significant, and containsenough data points to try a baseline reset, and the signal change isstill within the normal range, then the baseline should be reset and theinformation that the mean value has moved within the normal range iscaptured.

In alternative embodiments an additional set of four conditions could beused based on user input patient reference thresholds for the sign orlab test values.

The sequence 600 may also include operation 658, which comprises storingthe results for an i-window length of data points. The processing windowlength may be set to a constant, for example 30, during theinitialization operation 602. Additionally, the sequence 600 may includeoperation 660, which comprises writing data to an output buffer.

The sequence 600 may also include operation 662, which comprisesgenerating a data output signal that represents a statisticallysignificant change in the patient (or tissue) status. The operation ofgenerating a data output signal may comprise outputting results, for ani-window length of data points that includes the data point, wherein theoutputted results may include any of the following, all of thefollowing, or any combination of the following: (1) a time-line eventreport that includes at least some of the medical interventioninformation, (2) a Dynamic Change point Detection graph, and (3) a timesequenced table (also called a trend analysis report) containingstatistical information. The time sequenced table includes at least someinformation contained in the time-line display, the threshold testgraph, and the Dynamic Change point Detection graph. The time-line eventreport, the Dynamic Change point Detection graph, and the time sequencedtable containing statistical information, may be displayedsimultaneously on a single display. The outputting operation may furthercomprise outputting a threshold test graph. The outputted results may bedisplayed on one or more display devices such as a computer displayscreen or other type of video monitor (including a web browser), may beprinted on a printer, stored in a data archive, copied to a computerprogram (such as a word processor or presentation program), and/oremailed.

FIG. 7 shows an example of a time-line event graph (or report) 700. Thetime line event analysis (time line graph) is a one dimensional x-axisgraph that includes information gathered by the sign sensor 14, the labtest sensor 16, and/or the intervention data collector 18. Time may beindicated in days, and medical interventions such as changes inmedication or treatment plan, along with medical signs and/or lab testresults, may be shown in text windows along the x-axis. The user may bepermitted to add additional comments to these text windows. The textshown on the time line may be limited in length. A further explanatorytext field of information for each item may be made available through ahyperlink. The text associated with the time line graph may be added tothe patient's treatment record. The time-line graph may be presentedsimultaneously with a color coded Dynamic Change point Detection graph,thereby providing an attending medical physician an easy to read recordof the patient's current and past status, along with what correctiveactions have been taken. Using the time line graph in conjunction withthe Dynamic Change point Detection graph is particularly useful, becausemedical data monitoring entails more than merely determining when themedical data change is statistically significant, and it requiresunderstanding the clinical significance of the medical data changes.

FIG. 8 shows an example of a Dynamic Change point Detection graph 800.The Dynamic Change point Detection graph (data trend analysis graph)displays information that includes the results from the DCD algorithm.The graph may indicate a sign or lab test value or counts or rates ofoccurrence of an event on a Y-axis and days on a X-axis. In oneembodiment, the graph is color-coded. For example, a black bar mayindicate data points that are three or more standard deviations from themean. A red bar may indicate data points that are between two and threestandard deviations from the mean, or a statistically significant trendor shift. A yellow bar may indicate an increasing trend less than twostandard deviations from the mean. A green bar may indicate that thereis either no trend or a decreasing trend.

FIG. 9 shows an example of a data analysis report 900. The table (trendanalysis report) permits looking at information regarding actual datapoints in table format. The table may contain a number of columns. Adate column may specify the date a case occurred. Another column mayindicate a selected sign or lab test value or count or rate of an eventoccurring that day (daily incidence). Another column may include thecalculated mean, and another column may include the standard deviation.A trend direction column may be included to indicate whether the value,counts, or rates are increasing or decreasing relative to the mean. Atrend length column may be included to indicate the number of datapoints/days that are included in the current upward or downwardtrend/shift data set. A statistically significant event column may beincluded to indicate what type of statistical event has occurred.Possible events may include, for example, positive burst, negativeburst, positive outlier, negative outlier, positive trend/shift ornegative trend/shift mean.

FIG. 10 shows another example of a data analysis report 1000. The table(FIR SHEWHART CUSUM report) permits looking at information regardingactual data points in table format. The table may contain a number ofcolumns. A date column may specify the date a case occurred. Anothercolumn may indicate a selected sign or lab test value or count or rateof an event occurring that day (daily incidence). Another column mayinclude the calculated mean, and another column may include the standarddeviation. Another set of columns may present the number Shewhart teststatistic, Z, the lower maximum CUSUM test statistic, SL(I) and theupper maxium CUSUM test statistic, SH(I). A trend direction column maybe included to indicate whether the counts are increasing or decreasingrelative to the mean. A statistically significant event column may beincluded to indicate what type of statistical event has occurred. Atrend or signal length column may be included to indicate the number ofdata points/days that are included in the current upward or downwardtrend/shift data set. Possible events may include, for example, positiveburst, negative burst, positive outlier, negative outlier, positivetrend/shift or negative trend/shift mean. These events may also includeinformation on the direction of a trend relative to the patientreference value range. In addition, this information may also specifythe data points, if any, that are above or below the patient's thresholdrange, and may indicate if there has been no change within a userspecified length of time after an intervention. Also, columns containingthe calculated shift from the normal value, smoothed value, and upperand lower confidence intervals could be included. In addition, thepatient reference values and patient threshold values may be displayed.

FIG. 11 shows an example of a Dynamic Change point Detection graph 1100.The Dynamic Change point Detection graph (data trend analysis graph)displays information that includes the results from the DCD algorithm.The graph may indicate a sign or lab test value or counts or rates ofoccurrence of an event on a Y-axis and days on a X-axis. In oneembodiment, the graph is color-coded. For example, a white bar mayindicate data points that are three or more standard deviations from themean. A red bar may indicate data points that are between two and threestandard deviations from the mean, or a statistically significant trendor shift. A yellow bar may indicate an increasing trend less that isthan two standard deviations from the mean. A green bar may indicatethat there is either no trend or a decreasing trend. Data used in theprocess, as shown, may be collected (or received) at uneven timeintervals.

FIG. 12 shows another example of a Dynamic Change point Detection graph1200. The Dynamic Change point Detection graph (data trend analysisgraph) displays information that includes the results from the DCDalgorithm. The graph may be of the smoothed output values and may alsodisplay data points associated with statistically significant eventsand/or alarms. The graph may indicate a sign or lab test value or countsor rates of occurrence of an event on a Y-axis and days on a X-axis. Inone embodiment, the graph is color-coded. For example, a white circlemay indicate data points that are three or more standard deviations fromthe mean. A red circle may indicate data points that are between two andthree standard deviations from the mean, or a statistically significanttrend or shift. Data used in the process, as shown, may be collected (orreceived) at uneven time intervals.

A simple threshold test graph may also be outputted, and may bepresented with some or all of the other outputted information. Also, theresults for one or more days may be downloaded to a spreadsheet forfurther analysis.

Referring again to FIG. 6C, sequence 600 may also include operation 664,which comprises determining if the patient status monitoring is tocontinue or end. If the patient status monitoring is to continue,sequence 600 may also include operation 668, which comprises determiningwhether another data point is available for processing, or if theprocess must wait (and perhaps enter a sleep mode) until another datapoint is available. When another data point is available, the sequence600 repeats beginning at operation 606.

One exemplary embodiment may be described as a method for monitoringpatient status, comprising the following operations:

a) receiving and storing i^(th) data in an array, where i is an index;

b) determining a mean and a standard deviation from consistent data foruse with said i^(th) data, and identifying and flagging non-conformingdata from said array so that said consistent data exclude flaggednon-conforming data if: i) no a prior mean and standard deviation areavailable, ii) if there are less than C consistent data stored in saidarray, iii) if a prior determined standard deviation equals zero, or iv)if a mean and standard deviation reset are required and there is atleast C data in a subset of said array selected for use in said mean andstandard deviation reset, where C is a positive integer;

c) determining test statistics from said i^(th) data and said mean andstandard deviation determined in said step (b), wherein said teststatistics include a signal length;

d) flagging said i^(th) data if said i^(th) data is non-conforming;

e) determining a direction of any signal, shift, or trend indicated bysaid data array at said i^(th) data;

f) determining if any said signal, shift, or trend is indicated by saidi^(th) data stored in said array at said i^(th) data define astatistically significant signal, shift, or trend;

g) determining if a signal, shift, or trend is toward or away from apatient's reference range;

h) determining if a no signal present condition has lasted longer thanit should have after an intervention has been initiated;

i) displaying statistics representing said i^(th) data stored in saidarray;

j) returning to said step (a) when (i+1)^(th) data is available and if acontinue process instruction is received; and

k) generating a data output signal that represents a statisticallysignificant change in the patient status.

To summarize, an example of one embodiment is a windowed adaptiverecursive method for monitoring patient status, and tissue status, bydetecting non-conforming data, shifts, and trends in medical sign andlab data, capturing information on medical interventions, comparing theresults to the normal value range and/or to a user specified patientreference range, and displaying the results. One embodiment may bedescribed as a windowed adaptive recursive method for monitoring patientstatus, and tissue status, comprising the steps of: initializing theprocess; receiving information on medical interventions and medical signand lab data; detecting changes in incoming medical sign and lab data,calculating a mean and standard deviation if needed from the incomingdata or from a baseline data set identified for use in the calculationand identifying and removing non-conforming data points from the dataset, calculating various test statistics, testing to determine if thelast entered data point is non-conforming, determining the direction ofany signal present, determining if any trends and shifts in the data setare statistically significant, determining if detected shifts and trendsare toward or away from a reference range, determining if a no changecondition has lasted too long, resetting the baseline data set,detecting medical sign data points above or below a normal valuethreshold, marking the data point above the normal range, within thenormal range, and below the normal range, and storing and displaying thedata and results for the last window of data points and medicalinterventions.

Many embodiments give medical caregivers immediate feedback regardingthe status of a patient, and facilitate rapid implementation ofinterventions based on causal relationships between current treatmentsand medical sign and lab test changes. In view of the practice oftransporting less stable patients in combat environments to hospitals inrear echelons, the rapid response facilitated by improved patientmonitoring is beneficial because of the greater risk that a patient'scondition will change during transport. Also, constraints on medicalresources during transit make rapid responses to changes in a patient'scondition imperative. Further, a pre-specified integration time or aconstant data rate is not required. Therefore, data from unscheduledpatient visits and random measurements taken during combat evacuationscan be used, rather than requiring measurements to be taken according toa predetermined pattern. Because the methods and algorithms used forimplementing various embodiments may be used with low data rates, theseembodiments are particularly useful with the limited amounts of dataencountered in combat casualty care and ambulatory patient caresettings. Various embodiments could also be beneficially utilized formonitoring patients in a critical care facility.

Examples of computer implemented steps for implementing portions ofsequence 600 are provided in U.S. patent application Ser. No.10/423,568, filed 25 Apr. 2003, titled “Method and System for DetectingChanges in Data”, which is incorporated herein by this reference.However, it is to be understood that these computer implemented stepsmay also be written using other programming languages.

B2. Overall Sequence of Operation of Microsensor System

An example of a method embodiment is illustrated in FIGS. 21A-B, whichshow a sequence 2100 for a method for measuring and/or monitoring one ormore characteristics of tissue. As an example, the characteristic may betissue pH. Additionally or alternatively, the characteristic could beblood gas. Additionally, the temperature of the tissue could also bemeasured. For ease of explanation, but without any intended limitation,the example of FIGS. 21A-B is described in the context of themicrosensor system 1300 described above. The sequence 2100 may beginwith operation 2102, which comprises providing the microsensor system1300. The sequence 2100 may also include operation 2104, which comprisesopening a package containing the sterile microprobe 1302 and deliverysystem 1303, 1702 and removing the sterile microprobe 1302 and deliverysystem 1303, 1702 for subsequent use. The sequence 2100 may also includeoperation 2106, which comprises operationally coupling the controlmodule 1304 to the microprobe 1302, for example with electrical wiring.The sequence 2100, may also include operation 2108, which comprisesplacing a calibrant in contact with the gate 1408 of the ISFET 1406 andwith the reference electrode 1410 of the microprobe 1302.Calibration/testing of the microprobe 1302 is performed while themicroprobe 1302 is in contact with the calibrant 1418 (which may bepositioned within the microprobe delivery system 1303, 1702). Thesequence 2100 may also include operation 2110, which comprises measuringthe current flow between the source and the drain of the ISFET, as partof the calibration procedure. As part of the calibration/testing of themicroprobe 1302, the sequence 2100 may also include operation 2112,which comprises determining if the current flow is in an acceptablerange. If the current flow is not in an acceptable range, in operation2113 the microprobe 1302 may be discarded and replaced with anothermicroprobe 1302, and the process may resume at operation 2106. Built-intest algorithms may also be employed to reconfigure microprobe 1302 asnoted previously.

Sequence 2100 may also include operation 2114 which comprisespositioning the gate 1408 of the ISFET 1406, and the reference electrode1410, in tissue of interest (or in contact with, or in fluidcommunication with the tissue of interest). As an example, operation2114 may be accomplished by positioning the microprobe 1302 in thetissue of interest by actuating an actuator 1602, 1704 in a microprobedelivery system 1303, 1702. As an example, the tissue of interest may beskin (which may be in a casualty patient). As an example, themicrosensor (or microsensors) 1302 may be positioned in the dermis infingers or toes, which is a useful location for monitoring shock.Alternatively, a microsensor (or microsensors) 1302 could be positionednear the heart to detect heart blockage or congestive heart failure. Themicroprobe 1302 may be positioned in the dermis generally anywhere on apatient's body.

The sequence 2100 may also include operation 2116, which comprisesmeasuring the current flow between the source 1504 and the drain 1506 ofthe ISFET 1406. The sequence 2100 may also include operation 2118, whichcomprises sending a representation of the current flow to the controlmodule 1304. Although a single reading is useful, repeated or continuousmonitoring permits performing additional analysis, and permits applyingstatistical techniques on the data, such as the DCD process discussedabove and in the related applications referenced above. Although asingle reading from a microprobe 1302 that quickly fails would beuseful, a functioning microprobe 1302 could be left in the dermis for along period of time, for example several days, during which data couldbe gathered. The microsensor system 1300 may be utilized to provideconstant real time information during a medical procedure such as anamputation. In other applications, the microsensor system 1300 may beused to periodically monitor the condition of a patient (for examplewhile warming a patient).

The sequence 2100 may further include operation 2120, which comprisesoutputting results, which may include a representation of the pH of thetissue (and possibly other information concerning the tissue andanalysis of the data). The results could also include any of the outputdiscussed above with reference to the patient status monitor 10. As anexample, if multiple microprobes 1302 are simultaneously utilized andmonitored by the control module 1304, the results may include a threedimensional representation of the pH of the tissue (and possibly othercharacteristics of the tissue, such as temperature and/or blood gaslevels and/or blood flow information). In a specific example, ifmultiple microprobes 1302 are positioned in a patient's hand, theresults could include a three dimensional display of the hand showingcharacteristics of the dermis of the hand where the microprobes 1302 arelocated, and may also display an alert status on the three dimensiondisplay. In some examples a volumetric, holographic, stereoscopic orvirtual three dimensional display could be employed and manipulated bythe user for alternative views.

Thus, whether a single microprobe 1302 or multiple microprobes 1302 aremonitored, after the microprobe (or microprobes) 1302 is appropriatelypositioned within the desired tissue, for example using microprobedelivery system 1303, 1702, tissue pH and/or temperature, as well as,trends, shifts, variance and trends in the variance, of the pH and/ortemperature, and/or a trauma score, may be measured, computed, anddisplayed on the display 1812 of the control module 1304. To perform theanalysis of the data received from the microprobe 1302 (or from multiplemicroprobes 1302), the control module 1304 may perform calculationsdescribed above with regard to the patient status monitor 10, and/ordescribed in the related applications referenced above, and/or describedin U.S. Pat. No. 5,671,734, issued Sep. 30, 1997, titled “AutomaticMedical Sign Monitor”, which is incorporated herein by reference. Thecontrol module 1304 may also provide an estimate of the probability ofthe onset of shock and/or death. For example within a user specifiedperiod of time based on the application of survival analysis on raw dataand/or a trauma score. Depending on the particular hardware arrangement,the calculations and analysis may be performed by the control module1304, or by the patient status data processor 12, or by the controlmodule 1304 in conjunction with the patient status data processor 12.The sequence 2100 may also include operation 2122, which comprisesremoving the microprobe (or microprobes) 1302 from the tissue, anddisposing of the microprobe 1302 using an acceptable medical wastedisposal technique.

V. Other Embodiments

It will be understood that many additional changes in the details,materials, steps and arrangement of parts, which have been hereindescribed and illustrated to explain the nature of the invention, may bemade by those skilled in the art within the principle and scope of theinvention as expressed in the appended claims. Furthermore, althoughelements of the invention may be described or claimed in the singular,the plural is contemplated unless limitation to the singular isexplicitly stated.

1. A microsensor system, comprising: a control module; and at least onemicroprobe communicatively coupled to the control module, wherein eachmicroprobe comprises: a housing having an aperture; an ISFET attached tothe housing, wherein the ISFET has a gate located proximate theaperture; reference electrode attached to the housing proximate theaperture; and a signal bearing medium communicatively coupled to thecontrol module, tangibly embodying a program of machine-readableinstructions executable by a digital processing apparatus to perform amethod for monitoring a characteristic tissue, comprising: means forreceiving medical intervention information; means for receiving patientreference values; means for receiving a tissue data point; means forchecking validity of said tissue data point; means for determining abaseline mean and a baseline standard deviation for said tissue data ifthe baseline mean and the baseline standard deviation have not beendetermined for said tissue data; means for calculating test statistics;means for determining if a condition of said tissue data point isnon-conforming; means for flagging the condition of said tissue datapoint if the condition of said tissue data point is non-conforming;means for determining if a statistically significant positive ornegative trend is occurring; and means for generating a data outputsignal that represents a change in patient status.
 2. The microsensorsystem of claim 1 wherein said tissue data point is selected from thegroup consisting of: pH, and blood gas.